Artificial neural networks may be categorized in terms of their corresponding training algorithms. In a supervised network, training data is provided during a training phase. The training data consists of many pairs of known input/output patterns. In an unsupervised network, data consists of input data only.
One example of an unsupervised network is a Kohonen neural network. This means that the Kohonen network is presented with data, but the correct output that corresponds to that data is not specified. When a pattern of input data is presented to a Kohonen network one of the output neurons “fires” (i.e., is selected as a “winner”). This “firing” neuron is the output from the Kohonen network for that particular input pattern. Often these “firing” neurons represent groups in the data that is presented to the Kohonen network.